We propose a comprehensive model of how experiences are encoded and retrieved from memory. At the core of the model is a dynamic retrieval process incorporating two essential mechanisms: iterative retrieval, whereby information is sequentially sampled from memory to access the full history of experiences; and competitive retrieval, whereby the most prominent features in memory inhibit the recollection of other features. Together with context-based encoding, the model quantitatively explains well-known facts about response order and inter-response times in recall experiments. We show that our retrieval process maps closely to existing decision frameworks, such as drift–diffusion models, suggesting that the memory system plays a fundamental role in a wide-ranging set of decision-making settings.
- Faculty
- Publications
- Postdoctoral Scholars
-
Research Labs & Initiatives
- Cities, Housing & Society Lab
- Corporate Governance Research Initiative
- Corporations and Society Initiative
- Golub Capital Social Impact Lab
- Initiative for Financial Decision-Making
- Policy and Innovation Initiative
- Rapid Decarbonization Initiative
- Value Chain Innovation Initiative
- Venture Capital Initiative
- Behavioral Lab
- Data, Analytics & Research Computing